References arXiv papers for OCR models including DeepSeek-OCR and Qwen-Image-Layered, providing access to research documentation for the integrated OCR technologies.

Uses FastAPI as the backend framework for the WebApp interface, providing RESTful API server with async processing for document OCR operations.

Integrates multiple OCR models hosted on GitHub repositories including GOT-OCR2.0, providing access to state-of-the-art OCR engines and their source code.

Integrates multiple state-of-the-art OCR models from Hugging Face including DeepSeek-OCR, Florence-2, DOTS.OCR, PP-OCRv5, and Qwen-Image-Layered for comprehensive document processing capabilities.

Integrates PaddlePaddle's PP-OCRv5 OCR system for industrial-grade text extraction with high accuracy, fast inference, and edge deployment capabilities.

Uses Poetry for dependency management and installation of the OCR-MCP server and its required packages.

Leverages PyTorch for GPU-accelerated OCR model inference, enabling high-performance document processing with CUDA support.

1. Click on "Install Server".

2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.

3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@OCR-MCP scan this receipt and extract the total amount with DeepSeek-OCR"

That's it! The server will respond to your query, and you can continue using it as needed.

Here is a step-by-step guide with screenshots.